1,901 research outputs found
Why KDAC? A general activation function for knowledge discovery
Deep learning oriented named entity recognition (DNER) has gradually become
the paradigm of knowledge discovery, which greatly promotes domain
intelligence. However, the current activation function of DNER fails to treat
gradient vanishing, no negative output or non-differentiable existence, which
may impede knowledge exploration caused by the omission and incomplete
representation of latent semantics. To break through the dilemma, we present a
novel activation function termed KDAC. Detailly, KDAC is an aggregation
function with multiple conversion modes. The backbone of the activation region
is the interaction between exponent and linearity, and the both ends extend
through adaptive linear divergence, which surmounts the obstacle of gradient
vanishing and no negative output. Crucially, the non-differentiable points are
alerted and eliminated by an approximate smoothing algorithm. KDAC has a series
of brilliant properties, including nonlinear, stable near-linear transformation
and derivative, as well as dynamic style, etc. We perform experiments based on
BERT-BiLSTM-CNN-CRF model on six benchmark datasets containing different domain
knowledge, such as Weibo, Clinical, E-commerce, Resume, HAZOP and People's
daily. The evaluation results show that KDAC is advanced and effective, and can
provide more generalized activation to stimulate the performance of DNER. We
hope that KDAC can be exploited as a promising activation function to devote
itself to the construction of knowledge.Comment: Accepted by Neurocomputin
DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks
3D scene understanding is important for robots to interact with the 3D world
in a meaningful way. Most previous works on 3D scene understanding focus on
recognizing geometrical or semantic properties of the scene independently. In
this work, we introduce Data Associated Recurrent Neural Networks (DA-RNNs), a
novel framework for joint 3D scene mapping and semantic labeling. DA-RNNs use a
new recurrent neural network architecture for semantic labeling on RGB-D
videos. The output of the network is integrated with mapping techniques such as
KinectFusion in order to inject semantic information into the reconstructed 3D
scene. Experiments conducted on a real world dataset and a synthetic dataset
with RGB-D videos demonstrate the ability of our method in semantic 3D scene
mapping.Comment: Published in RSS 201
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